Unfair Inequality in Education: A Benchmark for AI-Fairness Research

Resumen

Unfair Inequality in Education: A Benchmark for AI-Fairness Research This is the repository for the code and dataset of the paper intitled "Unfair Inequality in Education: A Benchmark for AI-Fairness Research" submitted to the DEMO track of the 27TH European Conference on Artificial Intelligence (ECAI). Abstract This paper proposes a novel benchmark specifically designed for AI fairness research in education. It can be used for challenging tasks aimed at improving students' performance and reducing dropout rates which are also discussed in the paper to emphasize significant research directions. By prioritizing fairness, this benchmark aims to foster the development of bias-free AI solutions, promoting equal educational access and outcomes for all students. Structure benchmark contains: the proposed dataset (dataset.csv), and the mask for dealing with missing values (missing_mask.csv). raw_data includes: the original dataset (original.csv), and the intermediate stages of the pre-processing pipeline (split and pre_processed). res contains the documentation, including: the transformation mapping each column of the original dataset to the proposed one, along with the missingness category and original text (meta_data_mapping.csv), and the value type and domains of each column of the original, intermediate-stage, and proposed datasets (rispectively, meta_data_original.json and meta_data_merged.json and meta_data_final.json). src contains the source code for running the pre-processing and corresponding analysis: pre_processing and statscontain the code for the two corresponding tasks, and pre_processing.py and split.py are two entry points. Finally, Dockerfile and requirements.txt set up the environment for running the applications across multiple platforms and with Python, respectively.